{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Color Transfer (Multi-Reference Style Transfer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Append 'src' direc to import modules from notebooks directory#\n",
    "##################################################\n",
    "#import os,sys\n",
    "#src_dir=os.path.join(os.getcwd(),os.pardir)\n",
    "#sys.path.append(src_dir)\n",
    "#################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import time\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torch import optim\n",
    "import cv2\n",
    "from torchvision import transforms\n",
    "from torchvision.utils import make_grid\n",
    "from collections import OrderedDict\n",
    "from PIL import Image,ImageOps\n",
    "import torch.optim as optim\n",
    "import scipy\n",
    "from skimage import color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Source & Reference Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16d76cec88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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auXlBtZdkJhxCqa8TM85FQLmAJwfEIMFL+wzATt8J42sL3qV3idpTztXE5QDP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iaHKDl1trWOFwOBwTtVzKvCBUsLW5Slc15nRS+thKUNclZZ3jyhZCPCy+RdFYaUwmE+RByJtv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\nokSJBxilkS9RokSJBxj/A9GehKavWsGnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d6349828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgS_path = './image/city_basic.jpg'\n",
    "imgR_path='./image/city_night.jpg'\n",
    "origS = Image.open(imgS_path).convert(\"RGB\")\n",
    "imshow(origS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16d7d91e48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vg4m6AGNhXmdKHN3Ta1Tc2oW1jIRde4WMfNw9RhFKgJg9A3ZCFSqeAJaBcEr4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\nu7QSEOsgfiJcNrAaax++pgOy41bVQX0bjGiXc6ZpZqSoJum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BotsPNnT90ij4wZPTEnLG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\nOh6B6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h6J+XT8tqFS+Z08HhG3lXOnyHKoRHEAtF3YmqJDLND0ttAilhoijojjkISlELsh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\nEtjFLnaxi29i7CqBXexiF7v4Jsb/DyLnSbVWrXc6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d76fdb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "origR = Image.open(imgR_path).convert(\"RGB\")\n",
    "imshow(origR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Image Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\torchvision\\transforms\\transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.\n",
      "  \"please use transforms.Resize instead.\")\n"
     ]
    }
   ],
   "source": [
    "scale, tensorize, normalize = transforms.Scale((224,224)),transforms.ToTensor(),\\\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n",
    "    \n",
    "loader = transforms.Compose([\n",
    "    scale,tensorize,normalize\n",
    "])  #scale to 224 224, tensorize, normalize\n",
    "\n",
    "def image_loader(img_path,flip=False):\n",
    "    img = Image.open(img_path).convert(\"RGB\")\n",
    "    if flip:\n",
    "        img = img.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "    \n",
    "    image = loader(img)\n",
    "    image = Variable(image.unsqueeze(0)) #load image and make to Variable\n",
    "    \n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgS=image_loader(imgS_path,flip=False).float()\n",
    "imgR=image_loader(imgR_path,flip=False).float()\n",
    "\n",
    "imgS.data.size() #1,3,224,224\n",
    "imgR.data.size()\n",
    "imgS.data.dtype #made in to torch, float value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgSnp = np.squeeze(imgS.data.numpy())\n",
    "imgRnp = np.squeeze(imgR.data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 224, 224)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgSnp.shape # (3, 224, 224) if transposed(1,2,0) -> 224 224 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show(img):\n",
    "    npimg = img.numpy()\n",
    "    imshow(np.transpose(npimg, (1,2,0))) #,interpolation = 'nearest' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 224, 224])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgS.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.72480524,  0.72480524,  0.72480524, ...,  0.98167652,\n",
       "          0.94742703,  0.93030226],\n",
       "        [ 0.70768046,  0.70768046,  0.72480524, ...,  0.93030226,\n",
       "          0.89605278,  0.87892801],\n",
       "        [ 0.70768046,  0.70768046,  0.70768046, ...,  0.87892801,\n",
       "          0.84467852,  0.82755375],\n",
       "        ..., \n",
       "        [-1.41578913, -1.2616663 , -1.41578913, ..., -1.19316733,\n",
       "         -0.43967807,  0.15968838],\n",
       "        [-1.53566241, -1.41578913, -1.4329139 , ..., -2.06652975,\n",
       "         -1.87815738, -1.62128615],\n",
       "        [-1.67266035, -1.63841093, -1.53566241, ..., -2.06652975,\n",
       "         -2.06652975, -2.06652975]],\n",
       "\n",
       "       [[ 0.85294122,  0.85294122,  0.85294122, ...,  1.04551828,\n",
       "          1.01050425,  0.99299723],\n",
       "        [ 0.83543426,  0.83543426,  0.85294122, ...,  0.99299723,\n",
       "          0.95798326,  0.94047624],\n",
       "        [ 0.83543426,  0.83543426,  0.83543426, ...,  0.94047624,\n",
       "          0.90546227,  0.88795525],\n",
       "        ..., \n",
       "        [-1.19537807, -1.07282913, -1.17787099, ..., -1.03781509,\n",
       "         -0.37254897,  0.17016806],\n",
       "        [-1.24789906, -1.17787099, -1.19537807, ..., -1.7731092 ,\n",
       "         -1.66806722, -1.4229691 ],\n",
       "        [-1.33543408, -1.38795507, -1.28291309, ..., -1.73809516,\n",
       "         -1.79061615, -1.79061615]],\n",
       "\n",
       "       [[ 1.31538141,  1.31538141,  1.31538141, ...,  1.38509822,\n",
       "          1.35023987,  1.33281064],\n",
       "        [ 1.29795229,  1.29795229,  1.31538141, ...,  1.35023987,\n",
       "          1.31538141,  1.29795229],\n",
       "        [ 1.29795229,  1.29795229,  1.29795229, ...,  1.33281064,\n",
       "          1.29795229,  1.28052306],\n",
       "        ..., \n",
       "        [-0.67154676, -0.54954237, -0.53211319, ..., -0.70640522,\n",
       "         -0.13124175,  0.35677567],\n",
       "        [-0.5669716 , -0.49725482, -0.37525046, ..., -1.33385623,\n",
       "         -1.22928095, -1.02013063],\n",
       "        [-0.58440077, -0.65411752, -0.39267966, ..., -1.21185172,\n",
       "         -1.24671006, -1.24671006]]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgSnp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process\n",
    "From S_6 = S_L+1 to S_1(Final Result)\n",
    "\n",
    "Feature Domain\n",
    "    >FeatureExtractor\n",
    "    >PatchMatch\n",
    "    >BDS Voting\n",
    "\n",
    "Image Domain\n",
    "    >Resolution equal\n",
    "    >Local Color Transfer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FeatureExtractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (1): ReLU(inplace)\n",
      "  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (3): ReLU(inplace)\n",
      "  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (6): ReLU(inplace)\n",
      "  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (8): ReLU(inplace)\n",
      "  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (11): ReLU(inplace)\n",
      "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (13): ReLU(inplace)\n",
      "  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (15): ReLU(inplace)\n",
      "  (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (17): ReLU(inplace)\n",
      "  (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (20): ReLU(inplace)\n",
      "  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (22): ReLU(inplace)\n",
      "  (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (24): ReLU(inplace)\n",
      "  (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (26): ReLU(inplace)\n",
      "  (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (29): ReLU(inplace)\n",
      "  (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (31): ReLU(inplace)\n",
      "  (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (33): ReLU(inplace)\n",
      "  (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (35): ReLU(inplace)\n",
      "  (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model1 = models.vgg19(pretrained=True).features\n",
    "print(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReLU(inplace)\n",
      "ReLU(inplace)\n"
     ]
    }
   ],
   "source": [
    "relu_1 = model1[1] #1,6,11,20,29\n",
    "relu_2 = model1[6]\n",
    "relu_3 = model1[11]\n",
    "relu_4 = model1[20]\n",
    "relu_5 = model1[29]\n",
    "\n",
    "print(relu_1)\n",
    "print(relu_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FeatureExtractor(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(FeatureExtractor, self).__init__()\n",
    "\n",
    "    def add_layer(self,name, layer):\n",
    "        self.add_module(name, layer)\n",
    "    \n",
    "    def forward(self,x):\n",
    "        list = []\n",
    "        for module in self._modules:\n",
    "            x = self._modules[module](x)\n",
    "            list.append(x)\n",
    "        return list\n",
    "\n",
    "\n",
    "model_t  =  FeatureExtractor()  # the new Feature extractor module network\n",
    "i = 1\n",
    "conv_counter = 1\n",
    "relu_counter = 1\n",
    "pool_counter = 1\n",
    "batn_counter = 1\n",
    "\n",
    "block_counter = 1\n",
    "\n",
    "for layer in list(model1):\n",
    "    \n",
    "    if isinstance(layer, nn.Conv2d):\n",
    "        name = \"conv_\" + str(block_counter) + \"_\"+str(conv_counter)\n",
    "        conv_counter +=1\n",
    "        model_t.add_layer(name, layer)\n",
    "\n",
    "    if isinstance(layer, nn.ReLU):\n",
    "        name = \"relu_\" + str(block_counter) + \"_\"+str(relu_counter)\n",
    "        relu_counter +=1\n",
    "        model_t.add_layer(name, layer)\n",
    "\n",
    "    if isinstance(layer, nn.MaxPool2d):\n",
    "        name = \"pool_\" + str(block_counter) \n",
    "        pool_counter = bn_counter = relu_counter = conv_counter= 1\n",
    "        block_counter +=1\n",
    "        model_t.add_layer(name, nn.AvgPool2d((2,2)))  # ***\n",
    "        \n",
    "    if isinstance(layer, nn.BatchNorm2d):\n",
    "        name = \"batn_\" + str(block_counter) + \"_\"+str(batn_counter)\n",
    "        batn_counter +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FeatureExtractor(\n",
       "  (conv_1_1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_1_1): ReLU(inplace)\n",
       "  (conv_1_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_1_2): ReLU(inplace)\n",
       "  (pool_1): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n",
       "  (conv_2_1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_2_1): ReLU(inplace)\n",
       "  (conv_2_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_2_2): ReLU(inplace)\n",
       "  (pool_2): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n",
       "  (conv_3_1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_3_1): ReLU(inplace)\n",
       "  (conv_3_2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_3_2): ReLU(inplace)\n",
       "  (conv_3_3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_3_3): ReLU(inplace)\n",
       "  (conv_3_4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_3_4): ReLU(inplace)\n",
       "  (pool_3): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n",
       "  (conv_4_1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_4_1): ReLU(inplace)\n",
       "  (conv_4_2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_4_2): ReLU(inplace)\n",
       "  (conv_4_3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_4_3): ReLU(inplace)\n",
       "  (conv_4_4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_4_4): ReLU(inplace)\n",
       "  (pool_4): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n",
       "  (conv_5_1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_5_1): ReLU(inplace)\n",
       "  (conv_5_2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_5_2): ReLU(inplace)\n",
       "  (conv_5_3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_5_3): ReLU(inplace)\n",
       "  (conv_5_4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (relu_5_4): ReLU(inplace)\n",
       "  (pool_5): AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([512, 14, 14])\n",
      "torch.Size([512, 1, 14, 14])\n"
     ]
    }
   ],
   "source": [
    "reS =torch.squeeze(model_t(imgS.data)[-7]).data #Source into ReLu 5_1 == [-7]\n",
    "print(reS.size()) #512,14,14 C W H\n",
    "rsS = reS.view(-1,1,reS.size()[1],reS.size()[2]).data #resized\n",
    "print(rsS.size()) #512 1 14 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d445ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(make_grid(rsS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16800cd860>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OiHinwiW0AkMltQLDgDeb2SwingTe3uPmGcAd9ct3AJ+uqndEPBYRuydzPA1MaEbvfRkI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\nCuXwmxXK4TcrlMNvViiH36xQDr9Zof4HjkiaCVLiY1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1680005208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reSnp = reS.numpy().transpose(1,2,0) # 14 14 512  -> making this array to picture is obviously wrong\n",
    "reSnpImg = Image.fromarray(reSnp,'RGB')\n",
    "reSnpImg.save('processImage/reSnpImg.jpg')\n",
    "reSnpImg = imread('processImage/reSnpImg.jpg')\n",
    "imshow(reSnpImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 14, 512)\n"
     ]
    }
   ],
   "source": [
    "print(reSnp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([512, 14, 14])\n",
      "torch.Size([512, 1, 14, 14])\n"
     ]
    }
   ],
   "source": [
    "reR =torch.squeeze(model_t(imgR.data)[-7]).data #into layer\n",
    "print(reR.size()) #512,14,14\n",
    "rsR = reS.view(-1,1,reS.size()[1],reR.size()[2]).data #resized\n",
    "print(rsR.size()) #512 1 14 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d7787a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(make_grid(rsR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16801fb1d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RwIk0Zy5GRFdEvFvjEtqAvSS1AcOAN1vZLCIeB97pdfVZwIzm9zOAs+vqHREPRkR38+LT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168011e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reRnp = reR.numpy().transpose(1,2,0)\n",
    "reRnpImg = Image.fromarray(reRnp,'RGB')\n",
    "reRnpImg.save('processImage/reRnpImg.jpg')\n",
    "reRnpImg = imread('processImage/reRnpImg.jpg')\n",
    "imshow(reRnpImg) #actually no meaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## PatchMatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class PatchMatch: \n",
    "    def __init__(self, a, b, patchsize=3):\n",
    "        self.a = a\n",
    "        self.a_height = self.a.shape[1]    #C * H * W (e.g. 3 * 224 * 224)  == [0] * [1] * [2]\n",
    "        self.a_width = self.a.shape[2]\n",
    "        self.b = b\n",
    "        self.b_height = self.b.shape[1]\n",
    "        self.b_width = self.b.shape[2]\n",
    "        self.patchsize = patchsize\n",
    "        self.channels = self.a.shape[0]\n",
    "\n",
    "        self.aew = self.a.shape[2] - self.patchsize + 1 #number of patch that can be made in width\n",
    "        self.aeh = self.a.shape[1] - self.patchsize + 1 #number of patch that can be made in height\n",
    "        self.bew = self.b.shape[2] - self.patchsize + 1\n",
    "        self.beh = self.b.shape[1] - self.patchsize + 1\n",
    "        self.nnf = np.zeros((self.a.shape[1], self.a.shape[2], 2)).astype(np.int) #nnf build\n",
    "        self.nnd = np.zeros((self.a.shape[1], self.a.shape[2])) #nnf distance\n",
    "        self.init_nnf()\n",
    "\n",
    "    def init_nnf(self):\n",
    "        for ay in range(0, self.aeh):\n",
    "            for ax in range(0, self.aew):\n",
    "                bx = np.random.randint(0, self.bew - 1)\n",
    "                by = np.random.randint(0, self.beh - 1)\n",
    "                self.nnf[ay, ax, :] = [bx, by]\n",
    "                self.nnd[ay, ax] = self.calc_dist(ax, ay, bx, by)\n",
    "    \n",
    "    \n",
    "    def calc_dist(self, ax, ay, bx, by, cutoff = 2147483647):\n",
    "        \"\"\"\n",
    "        Measures distance between 2 patches across all channels\n",
    "        ax -- x coordinate of patch a\n",
    "        ay -- y coordinate of patch a\n",
    "        bx -- x coordinate of patch b\n",
    "        by -- y coordinate of patch b\n",
    "        cutoff\n",
    "        \"\"\"\n",
    "        num_pixels = 0\n",
    "        pixel_sum = 0\n",
    "        dmax = self.patchsize // 2  # dmax = 1\n",
    "        for dy in range(-dmax, dmax): #from middle pixel in patch to around pixel by -1~+1 \n",
    "            for dx in range(-dmax, dmax):\n",
    "                pixel_exists_in_a = (ay + dy) < self.a_height and (ay + dy) >= 0 and (ax + dx) < self.a_width and (ax + dx) >= 0\n",
    "                pixel_exists_in_b = (by + dy) < self.b_height and (by + dy) >= 0 and (bx + dx) < self.b_width and (bx + dx) >= 0\n",
    "                if pixel_exists_in_a and pixel_exists_in_b:\n",
    "                    for dc in range(0, self.channels):\n",
    "                        dp_tmp = self.a[dc, ay + dy, ax + dx] - self.b[dc, by + dy, bx + dx]\n",
    "                        pixel_sum += (dp_tmp*dp_tmp)\n",
    "\n",
    "                num_pixels += 1\n",
    "        ans = num_pixels / pixel_sum\n",
    "        if ans >= cutoff: return cutoff\n",
    "        return ans\n",
    "\n",
    "    def improve_guess(self, ax, ay, xbest, ybest, dbest, bx, by):\n",
    "        d = self.calc_dist(ax, ay, bx, by, dbest)\n",
    "        if d < dbest:\n",
    "            dbest = d\n",
    "            xbest = bx\n",
    "            ybest = by\n",
    "        return xbest, ybest, dbest\n",
    "\n",
    "    def improve_nnf(self, total_iter=5):\n",
    "        for iter in range(total_iter):\n",
    "            print(iter)\n",
    "            ystart, yend, ychange = 0, self.aeh, 1\n",
    "            xstart, xend, xchange = 0, self.aew, 1\n",
    "            if iter % 2 == 1:\n",
    "                ystart, yend, ychange = yend-1, -1, -1\n",
    "                xstart, xend, xchange = xend-1, -1, -1\n",
    "            for ay in range(ystart, yend, ychange):\n",
    "                for ax in range(xstart, xend, xchange):\n",
    "                    # best guess\n",
    "                    xbest, ybest = self.nnf[ay, ax]\n",
    "                    dbest = self.nnd[ybest, xbest]\n",
    "\n",
    "                    # propagation\n",
    "\n",
    "                    if 0 < ax - xchange < self.aew:\n",
    "                        xp, yp = self.nnf[ay, ax-xchange]\n",
    "                        if 0 < xp < self.bew:\n",
    "                            xbest, ybest, dbest = self.improve_guess(ax, ay, xbest, ybest, dbest, xp, yp)\n",
    "                    if 0 < ay - ychange < self.aeh:\n",
    "                        xp, yp = self.nnf[ay-ychange, ax]\n",
    "                        yp += ychange\n",
    "                        if 0 < yp < self.beh:\n",
    "                            xbest, ybest, dbest = self.improve_guess(ax, ay, xbest, ybest, dbest, xp, yp)\n",
    "\n",
    "                    # random search\n",
    "\n",
    "                    mag = max(self.b.shape[1], self.b.shape[2])\n",
    "\n",
    "                    while mag >= 1:\n",
    "                        xmin, xmax = max(xbest-mag, 0), min(xbest+mag+1, self.bew)\n",
    "                        ymin, ymax = max(ybest-mag, 0), min(ybest+mag+1, self.beh)\n",
    "                        xp = np.random.randint(xmin, xmax)\n",
    "                        yp = np.random.randint(ymin, ymax)\n",
    "                        xbest, ybest, dbest = self.improve_guess(ax, ay, xbest, ybest, dbest, xp, yp)\n",
    "                        mag = mag // 2\n",
    "\n",
    "                    self.nnf[ay, ax, :] = [xbest, ybest]\n",
    "                    self.nnd[ay, ax] = dbest\n",
    "\n",
    "    def solve(self):\n",
    "        self.improve_nnf(total_iter=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BDS Voting (Reconstruct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bds_vote(snn, rnn, snnd, rnnd, src, patchsize=3):\n",
    "    \"\"\"\n",
    "    Reconstructs an image or feature map by bidirectionaly\n",
    "    similarity voting\n",
    "    \"\"\"\n",
    "\n",
    "    src_height = src.shape[1]   # C * H * W\n",
    "    src_width = src.shape[2]\n",
    "    channels = src.shape[0]\n",
    "\n",
    "    dest = np.zeros(src.shape)\n",
    "\n",
    "    dest_height = src.shape[1]\n",
    "    dest_width = src.shape[2]\n",
    "\n",
    "    pmax = patchsize // 2\n",
    "\n",
    "    weights = np.zeros((dest_height, dest_width))\n",
    "\n",
    "    print(snn.shape)\n",
    "    ws = 1 / ((snn.shape[1] - patchsize + 1) * (snn.shape[2] - patchsize + 1))\n",
    "    wr = 1 / ((rnn.shape[1] - patchsize + 1) * (rnn.shape[2] - patchsize + 1))\n",
    "    # coherence\n",
    "    # The S->R forward NNF enforces coherence\n",
    "    for i in range(src_width): \n",
    "        for j in range(src_height):\n",
    "\n",
    "            px = snn[0, i, j]\n",
    "            py = snn[1, i, j]\n",
    "\n",
    "            for dy in range(j-pmax, j+pmax):\n",
    "                if j + dy < 0:\n",
    "                    continue\n",
    "                if j + dy >= dest_height:\n",
    "                    break\n",
    "                if py + dy < 0:\n",
    "                    continue\n",
    "                if py + dy >= src_height:\n",
    "                    break\n",
    "                for dx in range(i-pmax, i+pmax):\n",
    "                    if i + dx < 0:\n",
    "                        continue\n",
    "                    if i + dx >= dest_width:\n",
    "                        break\n",
    "                    if px + dx < 0:\n",
    "                        continue\n",
    "                    if px + dx >= src_width:\n",
    "                        break\n",
    "                    for ch in range(channels):\n",
    "                        dest[ch, dy + j, dx + i] += ws * src[ch, py + dy, px + dx]\n",
    "                    weights[dy + j, dx + i] += ws\n",
    "\n",
    "\n",
    "    # completeness\n",
    "    # The R->S backward NNF enforces completeness\n",
    "    for i in range(src_width):\n",
    "        for j in range(src_height):\n",
    "\n",
    "            px = rnn[0, i, j]\n",
    "            py = rnn[1, i, j]\n",
    "\n",
    "            for dy in range(j-pmax, j+pmax):\n",
    "                if j + dy < 0:\n",
    "                    continue\n",
    "                if j + dy >= src_height:\n",
    "                    break\n",
    "                if py + dy < 0:\n",
    "                    continue\n",
    "                if py + dy >= dest_height:\n",
    "                    break\n",
    "                for dx in range(i-pmax, i+pmax):\n",
    "                    if i + dx < 0:\n",
    "                        continue\n",
    "                    if i + dx >= src_width:\n",
    "                        break\n",
    "                    if px + dx < 0:\n",
    "                        continue\n",
    "                    if px + dx >= dest_width:\n",
    "                        break\n",
    "                    for ch in range(channels):\n",
    "                        dest[ch, py + dy, px + dx] += wr * src[ch, dy + j, dx + i]\n",
    "                    weights[py + dy, px + dx] += wr\n",
    "\n",
    "    for x in range(dest_width):\n",
    "        for y in range(dest_height):\n",
    "            s = 1 if weights[y, x] == 0 else (1 / weights[y, x])\n",
    "            for ch in range(channels):\n",
    "                dest[ch, y, x] *= s\n",
    "\n",
    "    return dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "snn = PatchMatch(reS.numpy(),reR.numpy()) #S->R\n",
    "snn.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snn.nnf.shape #(14,14,2)\n",
    "snn.nnd.shape #(14,14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "rnn = PatchMatch(reR.numpy(),reS.numpy()) #R->S\n",
    "rnn.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn.nnf.shape #(14,14,2)\n",
    "rnn.nnd.shape #(14,14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.2745,  0.2863,  0.2784,  0.2627,  0.2353,  0.2118,  0.1961,\n",
      "           0.2000,  0.2118,  0.2039,  0.1843,  0.1765,  0.1569,  0.1373],\n",
      "         [ 0.3373,  0.3529,  0.3529,  0.3412,  0.3098,  0.2745,  0.2549,\n",
      "           0.2549,  0.2510,  0.2431,  0.2353,  0.2235,  0.2157,  0.1961],\n",
      "         [ 0.4078,  0.4196,  0.4275,  0.4157,  0.3922,  0.3725,  0.3608,\n",
      "           0.3647,  0.3647,  0.3451,  0.3373,  0.3255,  0.2902,  0.2784],\n",
      "         [ 0.4196,  0.4275,  0.4196,  0.4196,  0.4353,  0.4863,  0.5255,\n",
      "           0.5255,  0.4941,  0.4745,  0.4588,  0.4157,  0.2667,  0.3098],\n",
      "         [ 0.5725,  0.5216,  0.4824,  0.4353,  0.4431,  0.4157,  0.5569,\n",
      "           0.5725,  0.5961,  0.5373,  0.5255,  0.4431,  0.2000,  0.2980],\n",
      "         [ 0.7922,  0.7333,  0.6549,  0.4745,  0.5686,  0.3020,  0.5098,\n",
      "           0.4784,  0.6353,  0.3216,  0.5882,  0.5333,  0.2235,  0.3059],\n",
      "         [ 0.8784,  0.8000,  0.7882,  0.5843,  0.7765,  0.3686,  0.6510,\n",
      "           0.5255,  0.6314,  0.2549,  0.6667,  0.7569,  0.2431,  0.1451],\n",
      "         [ 0.8549,  0.6000,  0.5490,  0.5255,  0.7255,  0.3804,  0.4902,\n",
      "           0.2588,  0.2078,  0.1490,  0.2745,  0.5725,  0.2549,  0.0471],\n",
      "         [ 0.4980,  0.3216,  0.3725,  0.4275,  0.5333,  0.3098,  0.3373,\n",
      "           0.2314,  0.1647,  0.1098,  0.1608,  0.3098,  0.2118,  0.0510],\n",
      "         [ 0.2353,  0.2471,  0.1686,  0.2667,  0.3490,  0.2275,  0.1451,\n",
      "           0.1922,  0.1490,  0.1059,  0.1490,  0.1608,  0.1647,  0.0706],\n",
      "         [ 0.1843,  0.1608,  0.1529,  0.2275,  0.3098,  0.2157,  0.1608,\n",
      "           0.2118,  0.1725,  0.1647,  0.2275,  0.2157,  0.2196,  0.0667],\n",
      "         [ 0.1569,  0.2039,  0.3412,  0.3412,  0.3333,  0.2824,  0.3216,\n",
      "           0.3882,  0.4510,  0.4902,  0.2549,  0.1373,  0.1451,  0.0471],\n",
      "         [ 0.1137,  0.1294,  0.1843,  0.1882,  0.1725,  0.1608,  0.2000,\n",
      "           0.2471,  0.3412,  0.2745,  0.1216,  0.1020,  0.0627,  0.0392],\n",
      "         [ 0.0941,  0.1137,  0.1059,  0.1216,  0.0667,  0.0549,  0.0706,\n",
      "           0.1216,  0.1843,  0.1843,  0.0902,  0.1373,  0.0784,  0.0824]],\n",
      "\n",
      "        [[ 0.2471,  0.2510,  0.2392,  0.2275,  0.2118,  0.2000,  0.1882,\n",
      "           0.1961,  0.2078,  0.1922,  0.1686,  0.1529,  0.1490,  0.1373],\n",
      "         [ 0.2784,  0.2902,  0.2941,  0.2824,  0.2627,  0.2353,  0.2157,\n",
      "           0.2157,  0.2118,  0.2039,  0.1882,  0.1804,  0.1765,  0.1686],\n",
      "         [ 0.3098,  0.3216,  0.3216,  0.3216,  0.3059,  0.2941,  0.2824,\n",
      "           0.2784,  0.2627,  0.2431,  0.2353,  0.2314,  0.2235,  0.2118],\n",
      "         [ 0.2784,  0.3020,  0.3020,  0.3098,  0.3137,  0.3412,  0.3608,\n",
      "           0.3608,  0.3373,  0.3333,  0.3333,  0.3098,  0.2078,  0.2314],\n",
      "         [ 0.3373,  0.3059,  0.2824,  0.2588,  0.2706,  0.2667,  0.3725,\n",
      "           0.3843,  0.3961,  0.3608,  0.3569,  0.3098,  0.1451,  0.2196],\n",
      "         [ 0.4627,  0.4157,  0.3451,  0.2824,  0.3137,  0.1843,  0.2980,\n",
      "           0.2941,  0.4118,  0.1843,  0.3765,  0.3490,  0.1569,  0.2118],\n",
      "         [ 0.5059,  0.4353,  0.4196,  0.3882,  0.5255,  0.2549,  0.4353,\n",
      "           0.3490,  0.4392,  0.1647,  0.4706,  0.5255,  0.1608,  0.0941],\n",
      "         [ 0.5922,  0.4431,  0.4118,  0.4118,  0.5647,  0.2902,  0.3647,\n",
      "           0.1922,  0.1608,  0.0980,  0.2157,  0.3804,  0.1569,  0.0157],\n",
      "         [ 0.3294,  0.2431,  0.2824,  0.3373,  0.4039,  0.2196,  0.2706,\n",
      "           0.2039,  0.1294,  0.0510,  0.1059,  0.2078,  0.1373,  0.0196],\n",
      "         [ 0.1725,  0.1647,  0.0941,  0.2000,  0.2863,  0.1725,  0.1098,\n",
      "           0.1608,  0.1059,  0.0471,  0.0941,  0.1020,  0.1059,  0.0314],\n",
      "         [ 0.1216,  0.0863,  0.0824,  0.1686,  0.2667,  0.1647,  0.1176,\n",
      "           0.1608,  0.1176,  0.0863,  0.1333,  0.1647,  0.1686,  0.0353],\n",
      "         [ 0.0824,  0.1137,  0.2118,  0.2627,  0.2196,  0.1843,  0.2039,\n",
      "           0.2431,  0.2941,  0.3137,  0.1412,  0.0902,  0.1059,  0.0196],\n",
      "         [ 0.0510,  0.0588,  0.0902,  0.0902,  0.0784,  0.0745,  0.0980,\n",
      "           0.1333,  0.2078,  0.1647,  0.0627,  0.0549,  0.0314,  0.0157],\n",
      "         [ 0.0392,  0.0431,  0.0392,  0.0431,  0.0196,  0.0157,  0.0235,\n",
      "           0.0588,  0.0980,  0.0980,  0.0392,  0.0745,  0.0353,  0.0353]],\n",
      "\n",
      "        [[ 0.5961,  0.5961,  0.5765,  0.5529,  0.5412,  0.5176,  0.4980,\n",
      "           0.5176,  0.5451,  0.5098,  0.4745,  0.4471,  0.4314,  0.4078],\n",
      "         [ 0.6235,  0.6431,  0.6471,  0.6314,  0.6000,  0.5529,  0.5255,\n",
      "           0.5255,  0.5216,  0.5059,  0.4824,  0.4667,  0.4588,  0.4431],\n",
      "         [ 0.6431,  0.6588,  0.6588,  0.6588,  0.6471,  0.6235,  0.6078,\n",
      "           0.5961,  0.5686,  0.5333,  0.5216,  0.5137,  0.4627,  0.4745],\n",
      "         [ 0.5647,  0.6039,  0.6118,  0.6275,  0.6314,  0.6275,  0.6784,\n",
      "           0.6824,  0.6549,  0.6510,  0.6627,  0.6157,  0.3294,  0.4588],\n",
      "         [ 0.6039,  0.5569,  0.5255,  0.4902,  0.5098,  0.3529,  0.6314,\n",
      "           0.6745,  0.7020,  0.6549,  0.6667,  0.5922,  0.2118,  0.4157],\n",
      "         [ 0.7373,  0.6706,  0.5765,  0.4118,  0.5020,  0.2431,  0.4863,\n",
      "           0.4745,  0.6549,  0.2706,  0.6275,  0.6039,  0.2118,  0.3686],\n",
      "         [ 0.6784,  0.5961,  0.5725,  0.4549,  0.6549,  0.2941,  0.5647,\n",
      "           0.4627,  0.5804,  0.1961,  0.6235,  0.7098,  0.2039,  0.1373],\n",
      "         [ 0.6000,  0.4667,  0.4078,  0.4000,  0.5490,  0.2745,  0.3725,\n",
      "           0.2078,  0.1843,  0.1098,  0.2275,  0.4235,  0.1686,  0.0196],\n",
      "         [ 0.2863,  0.2353,  0.2157,  0.2902,  0.3294,  0.1725,  0.2039,\n",
      "           0.1765,  0.1216,  0.0588,  0.0941,  0.1608,  0.1137,  0.0196],\n",
      "         [ 0.1490,  0.1451,  0.0824,  0.1882,  0.2863,  0.1686,  0.0980,\n",
      "           0.1412,  0.0980,  0.0471,  0.0824,  0.0824,  0.0902,  0.0275],\n",
      "         [ 0.0902,  0.0706,  0.0627,  0.1176,  0.2196,  0.1451,  0.0941,\n",
      "           0.1294,  0.0941,  0.0627,  0.0902,  0.1451,  0.1490,  0.0314],\n",
      "         [ 0.0471,  0.0706,  0.0980,  0.1765,  0.1098,  0.1020,  0.1137,\n",
      "           0.1216,  0.1451,  0.1490,  0.0784,  0.0784,  0.0863,  0.0196],\n",
      "         [ 0.0275,  0.0314,  0.0471,  0.0431,  0.0431,  0.0431,  0.0588,\n",
      "           0.0667,  0.0980,  0.0745,  0.0431,  0.0392,  0.0275,  0.0235],\n",
      "         [ 0.0275,  0.0275,  0.0275,  0.0275,  0.0157,  0.0157,  0.0196,\n",
      "           0.0353,  0.0471,  0.0431,  0.0314,  0.0431,  0.0275,  0.0275]]])\n"
     ]
    }
   ],
   "source": [
    "#(512 14 14)  imgR = 3 224 224    reR = 512 14 14\n",
    "def image_to_tensor(img, img_transforms = []):\n",
    "    normalize = transforms.Normalize(\n",
    "        mean=[0.485, 0.456, 0.406],\n",
    "        std=[0.229, 0.224, 0.225]\n",
    "    )\n",
    "    transform = transforms.Compose(img_transforms + [\n",
    "        transforms.ToTensor(),\n",
    "        \n",
    "    ])\n",
    "    return transform(img)\n",
    "\n",
    "def resized_R(self, size):\n",
    "        return image_to_tensor(origR, [transforms.Resize(size)])\n",
    "def resized_S(self,size):\n",
    "        return image_to_tensor(origS,[transforms.Resize(size)])\n",
    "\n",
    "Rs_R = resized_R(origR, reR.size()[1:3])\n",
    "\n",
    "print(Rs_R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 14, 14])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rs_R.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16801974e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(Rs_R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.6588,  0.6706,  0.6863,  0.7059,  0.7137,  0.7216,  0.7216,\n",
      "           0.6941,  0.6902,  0.6980,  0.7294,  0.7333,  0.7373,  0.7020],\n",
      "         [ 0.6863,  0.7098,  0.7294,  0.7294,  0.7373,  0.7451,  0.7569,\n",
      "           0.7333,  0.7294,  0.7216,  0.7294,  0.7255,  0.7216,  0.6941],\n",
      "         [ 0.7373,  0.7608,  0.7490,  0.7490,  0.7686,  0.7961,  0.8039,\n",
      "           0.7804,  0.7608,  0.7412,  0.7412,  0.7451,  0.7451,  0.7020],\n",
      "         [ 0.8000,  0.8000,  0.8118,  0.5608,  0.5961,  0.7686,  0.7961,\n",
      "           0.8118,  0.7725,  0.7333,  0.7294,  0.7216,  0.7137,  0.6980],\n",
      "         [ 0.8000,  0.8353,  0.8471,  0.5882,  0.6314,  0.8078,  0.8471,\n",
      "           0.8902,  0.8353,  0.7725,  0.7843,  0.7451,  0.7373,  0.7333],\n",
      "         [ 0.8000,  0.8392,  0.8431,  0.5882,  0.5882,  0.8196,  0.8392,\n",
      "           0.8314,  0.8157,  0.7686,  0.7451,  0.7137,  0.7020,  0.6824],\n",
      "         [ 0.7961,  0.8078,  0.8157,  0.5373,  0.4510,  0.7961,  0.8392,\n",
      "           0.8353,  0.8275,  0.8039,  0.7020,  0.6824,  0.7255,  0.7059],\n",
      "         [ 0.7412,  0.7529,  0.7647,  0.4667,  0.3255,  0.7216,  0.7804,\n",
      "           0.7804,  0.7765,  0.7490,  0.5686,  0.6471,  0.7176,  0.6941],\n",
      "         [ 0.6314,  0.6667,  0.6941,  0.3765,  0.2039,  0.5569,  0.7098,\n",
      "           0.7255,  0.7255,  0.6941,  0.5373,  0.6510,  0.6824,  0.6431],\n",
      "         [ 0.5725,  0.5882,  0.6784,  0.3569,  0.1725,  0.4824,  0.7059,\n",
      "           0.6902,  0.6941,  0.6353,  0.4902,  0.6431,  0.6392,  0.5961],\n",
      "         [ 0.5451,  0.4471,  0.3569,  0.1608,  0.1373,  0.4078,  0.6078,\n",
      "           0.5412,  0.5294,  0.4235,  0.3843,  0.6510,  0.5922,  0.5490],\n",
      "         [ 0.4902,  0.4627,  0.2392,  0.0902,  0.1255,  0.3882,  0.5882,\n",
      "           0.5137,  0.4980,  0.4471,  0.4667,  0.6078,  0.6157,  0.4510],\n",
      "         [ 0.3961,  0.4588,  0.2431,  0.1176,  0.1098,  0.3451,  0.4667,\n",
      "           0.4471,  0.4157,  0.3882,  0.4706,  0.4980,  0.5176,  0.3176],\n",
      "         [ 0.3176,  0.4471,  0.2196,  0.0706,  0.0980,  0.3137,  0.2745,\n",
      "           0.2980,  0.3490,  0.3294,  0.4392,  0.4078,  0.2392,  0.1765]],\n",
      "\n",
      "        [[ 0.6706,  0.6941,  0.7216,  0.7490,  0.7608,  0.7608,  0.7686,\n",
      "           0.7608,  0.7569,  0.7529,  0.7608,  0.7529,  0.7412,  0.6980],\n",
      "         [ 0.7020,  0.7294,  0.7529,  0.7686,  0.7765,  0.7804,  0.7961,\n",
      "           0.7882,  0.7843,  0.7765,  0.7686,  0.7529,  0.7412,  0.7137],\n",
      "         [ 0.7529,  0.7725,  0.7647,  0.7608,  0.7804,  0.8039,  0.8078,\n",
      "           0.7961,  0.7843,  0.7725,  0.7686,  0.7725,  0.7686,  0.7294],\n",
      "         [ 0.8157,  0.8196,  0.8235,  0.5765,  0.6196,  0.8000,  0.8157,\n",
      "           0.8196,  0.8039,  0.7804,  0.7765,  0.7765,  0.7647,  0.7451],\n",
      "         [ 0.8314,  0.8588,  0.8667,  0.6039,  0.6471,  0.8392,  0.8588,\n",
      "           0.8824,  0.8431,  0.8000,  0.8039,  0.7765,  0.7686,  0.7608],\n",
      "         [ 0.8353,  0.8627,  0.8706,  0.6078,  0.6039,  0.8549,  0.8706,\n",
      "           0.8627,  0.8431,  0.8118,  0.7882,  0.7608,  0.7529,  0.7373],\n",
      "         [ 0.8431,  0.8549,  0.8667,  0.5725,  0.4706,  0.8392,  0.8824,\n",
      "           0.8784,  0.8706,  0.8510,  0.7569,  0.7373,  0.7843,  0.7647],\n",
      "         [ 0.8157,  0.8275,  0.8353,  0.5294,  0.3843,  0.7843,  0.8510,\n",
      "           0.8510,  0.8431,  0.8118,  0.6392,  0.7137,  0.8000,  0.7843],\n",
      "         [ 0.7176,  0.7490,  0.7765,  0.4549,  0.2784,  0.5725,  0.7725,\n",
      "           0.7922,  0.7804,  0.7294,  0.5843,  0.6784,  0.7608,  0.7490],\n",
      "         [ 0.6039,  0.6353,  0.7137,  0.4235,  0.2431,  0.4745,  0.7216,\n",
      "           0.7137,  0.7176,  0.6392,  0.5020,  0.6235,  0.6824,  0.6824],\n",
      "         [ 0.5608,  0.5059,  0.4314,  0.2549,  0.2196,  0.4157,  0.6000,\n",
      "           0.5647,  0.5569,  0.4353,  0.4078,  0.6196,  0.5843,  0.5608],\n",
      "         [ 0.4941,  0.4941,  0.3294,  0.2000,  0.2078,  0.3922,  0.5804,\n",
      "           0.5608,  0.5216,  0.4471,  0.4667,  0.5804,  0.6039,  0.4824],\n",
      "         [ 0.3961,  0.4745,  0.3137,  0.2078,  0.2000,  0.3569,  0.4824,\n",
      "           0.5176,  0.4549,  0.3922,  0.4588,  0.4784,  0.5059,  0.3412],\n",
      "         [ 0.3333,  0.4627,  0.2706,  0.1686,  0.1922,  0.3294,  0.3333,\n",
      "           0.3686,  0.3804,  0.3451,  0.4275,  0.3882,  0.1922,  0.1490]],\n",
      "\n",
      "        [[ 0.7333,  0.7608,  0.7882,  0.8157,  0.8314,  0.8275,  0.8353,\n",
      "           0.8392,  0.8431,  0.8314,  0.8157,  0.8039,  0.7725,  0.7373],\n",
      "         [ 0.7686,  0.7843,  0.8039,  0.8275,  0.8392,  0.8392,  0.8549,\n",
      "           0.8549,  0.8549,  0.8471,  0.8275,  0.8078,  0.7882,  0.7647],\n",
      "         [ 0.8000,  0.8039,  0.8039,  0.7922,  0.8078,  0.8275,  0.8314,\n",
      "           0.8314,  0.8275,  0.8196,  0.8157,  0.8157,  0.8078,  0.7843],\n",
      "         [ 0.8510,  0.8510,  0.8510,  0.6196,  0.6667,  0.8392,  0.8431,\n",
      "           0.8431,  0.8392,  0.8314,  0.8314,  0.8353,  0.8235,  0.8039],\n",
      "         [ 0.8745,  0.8902,  0.8980,  0.6471,  0.6863,  0.8745,  0.8745,\n",
      "           0.8784,  0.8549,  0.8314,  0.8275,  0.8157,  0.8118,  0.8000],\n",
      "         [ 0.8824,  0.8941,  0.9020,  0.6510,  0.6471,  0.8902,  0.9020,\n",
      "           0.8941,  0.8784,  0.8549,  0.8353,  0.8157,  0.8078,  0.7961],\n",
      "         [ 0.8980,  0.9059,  0.9137,  0.6314,  0.5216,  0.8784,  0.9255,\n",
      "           0.9216,  0.9176,  0.8980,  0.8196,  0.7922,  0.8471,  0.8314],\n",
      "         [ 0.8824,  0.8902,  0.8980,  0.6039,  0.4667,  0.8392,  0.9098,\n",
      "           0.9098,  0.8980,  0.8706,  0.7255,  0.7765,  0.8706,  0.8627],\n",
      "         [ 0.7961,  0.8196,  0.8353,  0.5451,  0.3804,  0.6118,  0.8196,\n",
      "           0.8431,  0.8157,  0.7569,  0.6549,  0.7059,  0.8157,  0.8235],\n",
      "         [ 0.6667,  0.6941,  0.7451,  0.5020,  0.3490,  0.5216,  0.7333,\n",
      "           0.7255,  0.7294,  0.6588,  0.5451,  0.6078,  0.7137,  0.7412],\n",
      "         [ 0.6235,  0.5804,  0.5098,  0.3647,  0.3255,  0.4745,  0.6157,\n",
      "           0.5961,  0.5961,  0.4941,  0.4667,  0.5961,  0.5922,  0.5882],\n",
      "         [ 0.5412,  0.5490,  0.4275,  0.3137,  0.3137,  0.4510,  0.5804,\n",
      "           0.5922,  0.5529,  0.4863,  0.4980,  0.5647,  0.6000,  0.5216],\n",
      "         [ 0.4549,  0.5176,  0.4078,  0.3216,  0.3059,  0.4235,  0.5176,\n",
      "           0.5608,  0.5098,  0.4392,  0.4824,  0.4941,  0.5255,  0.3922],\n",
      "         [ 0.4000,  0.5098,  0.3608,  0.2863,  0.3020,  0.4039,  0.4157,\n",
      "           0.4471,  0.4431,  0.4039,  0.4588,  0.4157,  0.2667,  0.2235]]])\n"
     ]
    }
   ],
   "source": [
    "Rs_S = resized_S(origS, reS.size()[1:3])\n",
    "\n",
    "print(Rs_S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 14, 14])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rs_S.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tBbZGxEj38jFg6wrMAeCTwA9XovBqCP+Kk3Qe8F3gsxGR3k9seTWvB45HxFN11FugD3gP\n8PWIuAoYp3cve39D99h6F50noHcD6yTdXEfts4nOW161v+0l6S46h573110bVkf4jwAXz7u+rXtb\nLST10wn+/RHxcF11gWuAGyS9QudQ58OSvlNT7WFgOCLOvMp5iM6TQR2uA16OiBMR0QIeBj5QU+35\nXpP0LoDu38frLC7pVuB64BOxQu+3r4bw/xK4TNKlkgbonPx5pI7CkkTnuPdARHy5jppnRMSdEbEt\nIi6hc59/EhG17AEj4hjwqqTLuzddC+yvozadl/tXS1rbffyvZWVOeD4C3NK9fAvw/boKS9pJ53Dv\nhoiYqKvub4mIFf8DfJTOWc9fA3fVWPeDdF7uPQs80/3z0RW4/38CPFpzzT8E9nTv+78Cm2us/XfA\n88BzwL8Agz2u9wCd8wstOq96PgVcQOcs/4vAvwHn11j7IJ3zXGd+575R9+9cRPgTfmalWg0v+81s\nBTj8ZoVy+M0K5fCbFcrhNyuUw29WKIffrFAOv1mh/hcWvqgjDEadcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16808c0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(Rs_S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 14, 14)\n",
      "[[[ 0.23529412  0.58431374  0.77647061  0.36078434  0.36470589  0.16470589\n",
      "    0.07058824  0.04705882  0.          0.21568628  0.21960784  0.          0.\n",
      "    0.        ]\n",
      "  [ 0.38039216  0.50980395  0.47843139  0.52549024  0.63137256  0.21176471\n",
      "    0.19607843  0.26666668  0.30980393  0.32156863  0.58823533  0.53333336\n",
      "    0.22352942  0.22352942]\n",
      "  [ 0.30980393  0.65098044  0.40588238  0.27058825  0.20784314  0.53333337\n",
      "    0.25490197  0.2         0.29803922  0.25490197  0.66666667  0.48431375\n",
      "    0.21960784  0.24313725]\n",
      "  [ 0.16470589  0.65098044  0.52549024  0.52549024  0.63137256  0.54313725\n",
      "    0.14901961  0.14901961  0.27450983  0.25490197  0.66666667  0.57254903\n",
      "    0.21568628  0.20588237]\n",
      "  [ 0.49019609  0.49019609  0.25882355  0.25882355  0.20784314  0.16470589\n",
      "    0.10980392  0.26470589  0.16078432  0.14901961  0.27450983  0.30980393\n",
      "    0.21176471  0.21176471]\n",
      "  [ 0.75686279  0.33725493  0.23137255  0.23137255  0.16470589  0.16470589\n",
      "    0.10980392  0.47058824  0.65098044  0.10980392  0.16078432  0.3882353\n",
      "    0.45098039  0.04705882]\n",
      "  [ 0.57254903  0.14509805  0.19215687  0.19215687  0.14901961  0.14901961\n",
      "    0.12156863  0.38039216  0.49019609  0.10588236  0.14901961  0.24705883\n",
      "    0.34117648  0.05098039]\n",
      "  [ 0.44313727  0.22745099  0.14509805  0.33725493  0.23137255  0.66666667\n",
      "    0.75686279  0.33725493  0.23137255  0.57254903  0.35490197  0.12745098\n",
      "    0.27450983  0.07058824]\n",
      "  [ 0.56862751  0.21568628  0.21176472  0.19019609  0.19215687  0.27450983\n",
      "    0.57254903  0.14509805  0.19215687  0.47843139  0.39738564  0.28627452\n",
      "    0.23529412  0.18039217]\n",
      "  [ 0.10980392  0.30980393  0.33725493  0.16862746  0.26666668  0.40522876\n",
      "    0.29215688  0.22745099  0.14509805  0.14509805  0.24705884  0.3882353\n",
      "    0.48104576  0.37745099]\n",
      "  [ 0.10588236  0.22745099  0.14509805  0.15294118  0.22745099  0.35424837\n",
      "    0.24705883  0.21568628  0.16078432  0.16078432  0.31960784  0.33921571\n",
      "    0.37254902  0.31895427]\n",
      "  [ 0.25882355  0.32156863  0.3882353   0.3882353   0.45098039  0.47058826\n",
      "    0.30980393  0.28235296  0.22941177  0.07843138  0.52156865  0.41045753\n",
      "    0.45490199  0.40849674]\n",
      "  [ 0.23137255  0.2         0.24705883  0.24705883  0.34117648  0.34901963\n",
      "    0.22745099  0.14117647  0.19215687  0.          0.73333338  0.4013072\n",
      "    0.3385621   0.35294119]\n",
      "  [ 0.27450983  0.32156863  0.3882353   0.09019608  0.13725491  0.21176471\n",
      "    0.05098039  0.05098039  0.          0.          0.80000002  0.52156864\n",
      "    0.13725491  0.22941177]]\n",
      "\n",
      " [[ 0.1882353   0.3882353   0.52549024  0.28235296  0.27843138  0.10588236\n",
      "    0.03137255  0.01568628  0.          0.16470589  0.16862746  0.          0.\n",
      "    0.        ]\n",
      "  [ 0.29019611  0.29803922  0.29411766  0.34901963  0.43921568  0.2\n",
      "    0.1882353   0.20784314  0.23137255  0.18431373  0.3764706   0.34901963\n",
      "    0.15686276  0.15686276]\n",
      "  [ 0.21960784  0.43529413  0.30000002  0.2261438   0.16078432  0.34901961\n",
      "    0.21568628  0.14509805  0.21960784  0.16470589  0.47058824  0.36274512\n",
      "    0.17450981  0.16078432]\n",
      "  [ 0.08627451  0.43529413  0.34901963  0.34901963  0.43921568  0.33333335\n",
      "    0.09803922  0.09803922  0.21568628  0.16470589  0.47058824  0.38039216\n",
      "    0.15490197  0.15294118]\n",
      "  [ 0.31372552  0.36470589  0.19215687  0.19215687  0.16078432  0.12941177\n",
      "    0.05098039  0.16470589  0.10588236  0.09803922  0.21568628  0.20784314\n",
      "    0.13725491  0.13725491]\n",
      "  [ 0.52549024  0.27058825  0.20392158  0.20392158  0.12941177  0.12941177\n",
      "    0.05098039  0.29607845  0.43529413  0.05098039  0.10588236  0.24313725\n",
      "    0.29411766  0.01568628]\n",
      "  [ 0.38039216  0.10980392  0.16078432  0.16078432  0.10588236  0.10588236\n",
      "    0.09215687  0.29019611  0.36470589  0.04705882  0.09411765  0.13333334\n",
      "    0.20784314  0.01960784]\n",
      "  [ 0.27058825  0.17254902  0.10980392  0.27058825  0.20392158  0.47058824\n",
      "    0.52549024  0.27058825  0.20392158  0.38431375  0.21960785  0.06862745\n",
      "    0.21568628  0.03137255]\n",
      "  [ 0.31372552  0.16470589  0.17254902  0.16078432  0.16078432  0.21568628\n",
      "    0.38039216  0.10980392  0.16078432  0.29411766  0.27189544  0.20980393\n",
      "    0.18431374  0.15555556]\n",
      "  [ 0.05098039  0.21960784  0.27058825  0.09411765  0.2         0.2771242\n",
      "    0.22941177  0.17254902  0.10980392  0.10980392  0.18039216  0.27058824\n",
      "    0.35163399  0.27843139]\n",
      "  [ 0.04705882  0.17254902  0.10980392  0.08235294  0.16862746  0.25882354\n",
      "    0.19411765  0.16470589  0.11764706  0.11764706  0.23137256  0.24705884\n",
      "    0.23398694  0.19084968]\n",
      "  [ 0.19215687  0.20392158  0.24313725  0.24313725  0.29411766  0.35686275\n",
      "    0.21960784  0.18431373  0.13921569  0.03529412  0.30588237  0.27320263\n",
      "    0.32843138  0.29215688]\n",
      "  [ 0.20392158  0.09803922  0.13333334  0.13333334  0.20784314  0.28627452\n",
      "    0.17254902  0.06666667  0.09803922  0.          0.41568627  0.24052288\n",
      "    0.26143792  0.2627451 ]\n",
      "  [ 0.16470589  0.20392158  0.24313725  0.03921569  0.07450981  0.13725491\n",
      "    0.01960784  0.01960784  0.          0.          0.43529413  0.28039217\n",
      "    0.09019608  0.15294118]]\n",
      "\n",
      " [[ 0.48235295  0.45490198  0.65490197  0.60784315  0.59607844  0.09019608\n",
      "    0.02745098  0.01960784  0.          0.14509805  0.14901961  0.          0.\n",
      "    0.        ]\n",
      "  [ 0.27450983  0.4862745   0.4745098   0.46274509  0.58039221  0.5176471\n",
      "    0.49803921  0.32941178  0.45882354  0.27058825  0.62745103  0.60392162\n",
      "    0.21176471  0.21176471]\n",
      "  [ 0.17254902  0.56470592  0.52941177  0.46013073  0.18431373  0.64509806\n",
      "    0.52549024  0.21176471  0.41568627  0.19607843  0.62352944  0.61372553\n",
      "    0.35098039  0.20392158]\n",
      "  [ 0.0627451   0.56470592  0.46274509  0.46274509  0.58039221  0.43137255\n",
      "    0.10980392  0.10980392  0.22745099  0.19607843  0.62352944  0.42352942\n",
      "    0.30784315  0.30000002]\n",
      "  [ 0.14901961  0.37254904  0.20784314  0.20784314  0.18431373  0.12156863\n",
      "    0.05882353  0.31176472  0.09411765  0.10980392  0.22745099  0.16078432\n",
      "    0.11372549  0.11372549]\n",
      "  [ 0.70980397  0.20392158  0.17647059  0.17647059  0.12156863  0.12156863\n",
      "    0.05882353  0.44901964  0.56470592  0.05882353  0.09411765  0.12156863\n",
      "    0.14509805  0.01960784]\n",
      "  [ 0.42352942  0.09803922  0.14117648  0.14117648  0.09803922  0.09803922\n",
      "    0.22745099  0.27450983  0.37254904  0.04705882  0.08235294  0.06666667\n",
      "    0.09803922  0.01960784]\n",
      "  [ 0.50980395  0.16862746  0.09803922  0.20392158  0.17647059  0.62352944\n",
      "    0.70980397  0.20392158  0.17647059  0.67450985  0.36470588  0.06862745\n",
      "    0.22745099  0.02745098]\n",
      "  [ 0.50196083  0.14509805  0.32352943  0.31960786  0.14117648  0.22745099\n",
      "    0.42352942  0.09803922  0.14117648  0.4745098   0.45620916  0.35882354\n",
      "    0.34705884  0.36732029]\n",
      "  [ 0.05882353  0.17254902  0.20392158  0.08235294  0.1882353   0.27189544\n",
      "    0.21764706  0.16862746  0.09803922  0.09803922  0.2771242   0.45686276\n",
      "    0.6183007   0.50686277]\n",
      "  [ 0.04705882  0.16862746  0.09803922  0.0627451   0.11764706  0.34248367\n",
      "    0.15490196  0.14509805  0.09411765  0.09411765  0.3666667   0.40980394\n",
      "    0.36209151  0.28431374]\n",
      "  [ 0.20784314  0.11372549  0.12156863  0.12156863  0.14509805  0.48627452\n",
      "    0.17254902  0.10196079  0.07843137  0.02745098  0.55686277  0.35816996\n",
      "    0.33627452  0.31307191]\n",
      "  [ 0.17647059  0.05882353  0.06666667  0.06666667  0.09803922  0.28627452\n",
      "    0.16862746  0.03921569  0.05294118  0.          0.67058826  0.30065361\n",
      "    0.21699348  0.21568628]\n",
      "  [ 0.07450981  0.11372549  0.12156863  0.03137255  0.04313726  0.11372549\n",
      "    0.01960784  0.01960784  0.          0.          0.59607844  0.3254902\n",
      "    0.07843138  0.13529412]]]\n"
     ]
    }
   ],
   "source": [
    "G = bds_vote(snn.nnf.transpose(2,1,0), rnn.nnf.transpose(2,1,0), snn.nnd.transpose(1,0), rnn.nnd.transpose(1,0),Rs_R)\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x168050b080>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/henfaj/puyRswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1680066cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(np.transpose(G,(2,1,0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Affine Transfrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 68.22041515,   3.09829757,  -7.06738869],\n",
       "        [ 68.22041515,   3.09829757,  -7.06738869],\n",
       "        [ 68.22041515,   3.09829757,  -7.06738869],\n",
       "        ..., \n",
       "        [ 71.68514622,   3.08095938,  -2.93812813],\n",
       "        [ 71.68514622,   3.08095938,  -2.93812813],\n",
       "        [ 71.31505123,   3.08425348,  -2.9411943 ]],\n",
       "\n",
       "       [[ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        [ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        [ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        ..., \n",
       "        [ 70.94456415,   3.08756869,  -2.94427955],\n",
       "        [ 70.57368246,   3.09090524,  -2.9473841 ],\n",
       "        [ 70.57368246,   3.09090524,  -2.9473841 ]],\n",
       "\n",
       "       [[ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        [ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        [ 67.84661992,   3.10224993,  -7.07511646],\n",
       "        ..., \n",
       "        [ 69.62476451,   4.02949357,  -4.36347497],\n",
       "        [ 69.62476451,   4.02949357,  -4.36347497],\n",
       "        [ 69.25249493,   4.03396516,  -4.36813823]],\n",
       "\n",
       "       ..., \n",
       "       [[ 20.86567018,   1.91109721, -10.84100483],\n",
       "        [ 21.29472416,   2.11762742, -12.89434885],\n",
       "        [ 27.35524995,   2.91002951, -12.09621092],\n",
       "        ..., \n",
       "        [ 17.46691202,  -0.46958063,   1.30245065],\n",
       "        [ 31.42978877,  -0.43507284,   2.53463457],\n",
       "        [ 40.44179913,   0.18868427,   3.2833595 ]],\n",
       "\n",
       "       [[ 17.18149446,   6.48829052, -22.80599363],\n",
       "        [ 17.4147401 ,   2.44666712, -18.343504  ],\n",
       "        [ 17.42530608,   4.57097807, -18.30305826],\n",
       "        ..., \n",
       "        [  2.96280869,   1.21198486, -11.90449679],\n",
       "        [  2.71777773,   1.31389178, -11.47629561],\n",
       "        [  2.71777773,   1.31389178, -11.47629561]],\n",
       "\n",
       "       [[ 15.33989713,   6.86939362, -23.01509183],\n",
       "        [ 16.49184891,   2.58438364, -18.42998223],\n",
       "        [ 14.62466747,   4.96262422, -18.59598591],\n",
       "        ..., \n",
       "        [  4.31057706,   1.70884073, -12.97369227],\n",
       "        [  4.31057706,   1.70884073, -12.97369227],\n",
       "        [  4.31057706,   1.70884073, -12.97369227]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###example of rgb2lab but can't use in here bacause different initilization process.\n",
    "origSnp = np.asarray(origS)/255 ##0~255를 0~1로 만든다 rgb2lab엔 range detection이 없어서\n",
    "origS_lab = color.rgb2lab(origSnp)\n",
    "origS_lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 400, 3)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origSnp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14, 3)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npRss = np.asarray(Rs_S)\n",
    "npRss = np.transpose(npRss,(2,1,0))\n",
    "labRss = color.rgb2lab(npRss)\n",
    "labRss.shape #14 14 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 25.27283368,  33.73492487,  25.78140882,   9.82617388,\n",
       "         38.26678793,  63.11515513,  46.63706716,  36.70380994,\n",
       "         43.23646065,   5.14212747,   4.72016603,  22.3451206 ,\n",
       "         22.21471056,  20.06534862],\n",
       "       [ 47.72038147,  40.19780228,  53.48240037,  53.48240037,\n",
       "         42.78668604,  30.59954942,  11.2339076 ,  19.53801373,\n",
       "         18.37308895,  25.78140882,  19.53801373,  24.76176592,\n",
       "         12.18046152,  24.76176592],\n",
       "       [ 63.21692859,  38.81321411,  37.66320644,  43.53921885,\n",
       "         22.3451206 ,  22.21471056,  17.30280965,  11.2339076 ,\n",
       "         20.83678434,  30.59954942,  11.2339076 ,  29.90531543,\n",
       "         16.70705085,  29.90531543],\n",
       "       [ 36.5105075 ,  43.53921885,  28.24680487,  43.53921885,\n",
       "         22.3451206 ,  22.21471056,  17.30280965,  30.59954942,\n",
       "         19.34128499,  10.71222464,   9.03447217,  29.90531543,\n",
       "         16.70705085,   3.76504544],\n",
       "       [ 36.17647112,  53.28114294,  18.13463245,  53.28114294,\n",
       "         18.13463245,  13.7032093 ,  11.03691484,  22.21471056,\n",
       "         17.30280965,  23.03372028,  18.89082612,  35.61869147,\n",
       "         25.69286467,   7.6537241 ],\n",
       "       [ 11.53912344,  26.20772687,  45.74033532,  42.86820356,\n",
       "         13.7032093 ,  13.7032093 ,  11.03691484,  56.56839339,\n",
       "         24.765379  ,  33.56679072,  31.30222421,  42.65874358,\n",
       "         32.63526413,  15.87703133],\n",
       "       [  2.86913647,  24.71416645,  28.3189197 ,  10.4925422 ,\n",
       "          5.14212747,   5.14212747,  10.77371937,  63.11515513,\n",
       "         46.63706716,  26.25476033,  21.74612346,  25.78140882,\n",
       "         19.53801373,   1.85248815],\n",
       "       [  1.58951772,  24.99925524,  16.95694576,  10.4925422 ,\n",
       "         21.72212196,  38.41102409,  33.73492487,  30.59954942,\n",
       "         11.2339076 ,  19.53801373,  18.37308895,  21.89241791,\n",
       "          7.20803209,   1.85248815],\n",
       "       [  0.        ,  29.59934137,  27.85975994,  24.765379  ,\n",
       "         11.42375244,  53.48240037,  42.78668604,  22.21471056,\n",
       "         17.30280965,  11.2339076 ,  12.39018127,  16.48897192,\n",
       "         11.81554962,   0.        ],\n",
       "       [ 18.37308895,  24.50299353,  20.0847786 ,  20.0847786 ,\n",
       "         10.4925422 ,   5.14212747,   4.72016603,  49.28339209,\n",
       "         38.81321411,  11.2339076 ,  12.39018127,   3.24705568,\n",
       "          0.        ,   0.        ],\n",
       "       [ 18.83678792,  48.74579843,  56.56839339,  56.56839339,\n",
       "         24.765379  ,  11.42375244,   9.95016701,  28.94783963,\n",
       "         34.70592832,  21.80460169,  28.63924486,  41.8275622 ,\n",
       "         55.9019184 ,  58.48606662],\n",
       "       [  0.        ,  45.23779667,  44.8101524 ,  46.63706716,\n",
       "         24.8553584 ,  29.90531543,  16.70705085,   6.99003781,\n",
       "         26.07650256,  34.35829826,  30.80005442,  34.15590259,\n",
       "         31.22886205,  38.16810877],\n",
       "       [  0.        ,  18.60068998,  21.61321021,  19.44747167,\n",
       "         15.87703133,  35.61869147,  25.69286467,  24.765379  ,\n",
       "         22.676349  ,  44.08878173,  30.42088875,  39.04199127,\n",
       "         30.03082531,   9.16260432],\n",
       "       [  0.        ,  18.60068998,  19.46635841,  18.89924986,\n",
       "         15.87703133,   1.58951772,   1.85248815,   2.86913647,\n",
       "         19.54851139,  35.19926711,  24.97572132,  34.99285864,\n",
       "         30.55460433,  17.84705511]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npG = np.asarray(G)\n",
    "npG = np.transpose(npG,(2,1,0))\n",
    "labG = color.rgb2lab(npG)\n",
    "labG.shape #14 14 3\n",
    "labGl,labGa, labGb = labG[: , : ,0],labG[: , : , 1], labG[: , : , 2]\n",
    "labGl #14 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ],\n",
       "       [  0.        ,  40.65367785,  37.39119444,  36.35260037,\n",
       "         36.24175587,  32.72242157,  28.73099231,  25.01064996,\n",
       "         23.49390566,  19.94016745,  19.336375  ,  18.17850136],\n",
       "       [  0.        ,  43.26560716,  42.50038722,  38.603353  ,\n",
       "         33.67408176,  23.14949163,  20.24888536,  18.41801733,\n",
       "         20.77953466,  18.38341496,  21.27466369,  18.88592817],\n",
       "       [  0.        ,  39.50912521,  40.00420003,  31.91434423,\n",
       "         29.03523163,  18.51111536,  18.64703691,  18.57462008,\n",
       "         20.65272677,  17.88728658,  22.10378029,  21.52172162],\n",
       "       [  0.        ,  33.26399593,  39.42649185,  31.77703337,\n",
       "         27.05473965,  15.90897009,  22.0422691 ,  23.35208515,\n",
       "         26.45609585,  20.88330353,  26.08033429,  26.93838362],\n",
       "       [  0.        ,  27.44240608,  33.67097916,  26.20174949,\n",
       "         19.57448933,  11.37511826,  23.03270602,  29.27234044,\n",
       "         34.82875403,  27.05552233,  28.76147655,  28.20712891],\n",
       "       [  0.        ,  20.32612522,  25.64340414,  21.71521639,\n",
       "         17.96412306,  17.04104207,  29.23166865,  32.05166787,\n",
       "         34.6976685 ,  25.93526168,  26.79039686,  24.57059076],\n",
       "       [  0.        ,  17.43411585,  22.02209465,  17.46378785,\n",
       "         20.11933525,  24.73543156,  33.36225526,  30.93316998,\n",
       "         27.5699868 ,  20.52331775,  19.29987489,  17.24819864],\n",
       "       [  0.        ,  18.21840901,  22.14953047,  18.20917786,\n",
       "         21.77962982,  24.65730505,  31.15277566,  27.85437338,\n",
       "         23.4948236 ,  16.94546798,  14.0875251 ,  11.53394208],\n",
       "       [  0.        ,  27.17454913,  34.30884625,  28.06812851,\n",
       "         24.23872277,  19.35410811,  25.32791574,  27.63610149,\n",
       "         26.17114569,  20.94599738,  17.69506823,  20.30007391],\n",
       "       [  0.        ,  30.79553221,  40.36001691,  33.87409368,\n",
       "         25.5416349 ,  15.32909543,  18.11887208,  24.02158871,\n",
       "         28.0237469 ,  26.53577034,  24.27297873,  26.46564238],\n",
       "       [  0.        ,  28.26809211,  39.80321926,  34.57138411,\n",
       "         29.45538448,  21.64395673,  21.11123314,  21.83467987,\n",
       "         27.15707978,  30.39673884,  33.90414731,  35.78302776]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanGl = np.zeros((12,12))\n",
    "sumGl =np.zeros((12,12))\n",
    "stdGl = np.zeros((12,12))\n",
    "\n",
    "#make patch\n",
    "#patch -> mean, std\n",
    "for gy in range (1,12):\n",
    "    for gx in range (1,12):\n",
    "        meanGl[gy,gx] = (labGl[gy,gx]+labGl[gy-1,gx]+labGl[gy+1,gx]\n",
    "                         +labGl[gy,gx-1]+labGl[gy-1,gx-1]+labGl[gy+1,gx-1]\n",
    "                         +labGl[gy,gx+1]+labGl[gy-1,gx+1]+labGl[gy+1,gx+1])/9\n",
    "        stdGl[gy,gx] = (labGl[gy,gx]-meanGl[gy,gx]+labGl[gy-1,gx]+labGl[gy+1,gx]\n",
    "                         +labGl[gy,gx-1]+labGl[gy-1,gx-1]+labGl[gy+1,gx-1]\n",
    "                         +labGl[gy,gx+1]+labGl[gy-1,gx+1]+labGl[gy+1,gx+1])\n",
    "\n",
    "meanGl #12 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##in patch, need to calculate a,b from (pixel p and nearby 8pixel = q)\n",
    "class LocalColorTransfer:\n",
    "    def __init__(self, source, guide, patchsize=3):\n",
    "        self.s = source\n",
    "        self.s_height = self.source.shape[1]    #C * H * W (e.g. 3 * 14* 14)  == [0] * [1] * [2]\n",
    "        self.s_width = self.source.shape[2]\n",
    "        self.g = guide\n",
    "        self.g_height = self.guide.shape[1]\n",
    "        self.g_width = self.guide.shape[2]\n",
    "        self.patchsize = patchsize\n",
    "        self.channels = self.source.shape[0]\n",
    "\n",
    "        self.sew = self.source.shape[2] - self.patchsize + 1 #number of patch that can be made in width\n",
    "        self.seh = self.source.shape[1] - self.patchsize + 1 #number of patch that can be made in height\n",
    "        self.gew = self.guide.shape[2] - self.patchsize + 1\n",
    "        self.geh = self.guide.shape[1] - self.patchsize + 1\n",
    "        self.patchlab()\n",
    "    \n",
    "    def patchlab(self): ##initialize a and b from source and guidance by lab space using mean and std\n",
    "        for sy in range (1, self.seh):\n",
    "            for sx in range (1,self.sew):\n",
    "            \n",
    "        "
   ]
  }
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